The Identification of Multiple Outliers
本文定义了异常值相对于正常观测模型的位置，比较了基于稳健统计和向外检验的方法，发现稳健统计方法在最坏情况下的表现更优，并给出了一个具体的异常值识别方法。
Abstract One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown points in a sense derived from Donoho and Huber. But a more detailed analysis shows that methods based on robust statistics perform better with respect to worst-case behavior. A concrete outlier identifier based on a suggestion of Hampel is given.